AI tools keep getting better. But every session starts from zero. Your agent doesn't remember what the client rejected, what your design principles are, or the research behind the strategy. You re-explain everything, every time.
My biggest takeaways from @danshipper:
1. The future of work will happen inside Codex or Claude Code. Instead of putting AI into your SaaS tool, you’ll use your SaaS tools inside your favorite AI agents' in-app browser. Dan spends all his time in Codex now—writing documents, managing email, doing research, everything. He's using Google Docs, PostHog, and everything he needs within the agent's in-app browser. The agent can see what he’s doing, and has all of his context, so he and his agent collaborate quickly and super effectively.
2. Automation is a lie—every automation needs a human. Dan's company doubled in size this year despite being incredibly AI-forward. Why? Because in order to make automation work well, you need humans making sure everything keeps working. This is why benchmarks are misleading—they measure AI on problems we’ve already framed and can score, but there’s always a higher frame.
3. PMs will win the AI era. Marcus, a former PM who previously ran Axios’s writing product, joined Every after getting super AI-pilled. Now he runs their product Spiral, and ships faster than anyone on the team. He pairs technical knowledge with spiky product sense, deep user empathy, and an eye for what matters. Dan thinks any PM who gets really AI-native will be incredibly dangerous because the building is done for you—what matters is figuring out what to build and if it’s great.
4. Full-stack designers are becoming superheroes. Designers used to make beautiful interactions that engineers didn’t want to build or couldn’t execute properly. Now designers don’t need to hand things off; they can build it themselves. Designers are naturally creative people, and AI is the perfect tool for them because it lets them bring their vision to life without the traditional bottlenecks.
5. SaaS is not dead. In fact, Dan is bullish on SaaS stocks. When users bring their own AI (via Codex or Claude Code) to use SaaS products, the user—not the SaaS company—pays for tokens. This saves SaaS company’s margins. Since the agents need their own seats, Dan predicts that agents will create massive new demand for SaaS because there will be tons of agents using these products at high volume.
6. Every company will have one “super-agent” inside their Slack that every employee will use. Dan initially thought every employee would have their personal work agent, like a shadow AI org chart, but he’s completely flipped his view. He realized agents need humans who care about them. When someone gets tired of maintaining their personal agent, it becomes useless. The winning model is one forward-deployed engineer or AI-savvy person who maintains a company-wide agent (like Shopify’s River or Viktor), and then it trickles down to more specialized team agents as models improve and become less fiddly.
7. The AI job apocalypse is not happening, but you do need to evolve to stay relevant. Models make yesterday’s human competence cheap. But because everyone uses the same models, it all looks the same if you use it the default way; it becomes commoditized slop. Humans then take that frozen competence and use it to make something new and interesting for their specific situation. The key: “ride the models”—use them for everything you do, try new models when they drop, keep turning over rocks.
8. We will read way more AI-generated writing, and we will like it. Human writing is incredibly important for things that matter, but for internal docs, planning, and email, AI-generated is often better because most people are bad at writing strategy documents.
9. Build software for humans and agents to use together. The current model is building a CLI that an agent uses independently. Instead, you and your agent should be using the app together. This creates new design challenges—agents can make a billion requests in three seconds, so you need approval flows, inboxes that summarize what happened, logs, and easy rollback.
10. Forward-deployed engineers are the new most essential role. The big model companies have teams of people managing their internal agents, and those teams aren’t going away. It’s different from traditional software building, and certain engineers love it. As models get better, this role will evolve—you’ll be managing more agents doing more things.
@druids01 Looking forward to this. Does any of this scale across multiple design systems, e.g. a studio with five client systems running at once? Or is it inherently per-product?
The screen is doing a lot of work right now. Voice, vision, and space are modalities people and agents can share more naturally — AR being the first form factor that points at it. What comes after is harder to picture.
feels like a good time to seriously rethink how operating systems and user interfaces are designed
(also the internet; there should be a protocol that is equally usable by people and agents)
@stainlu@Teknium@NousResearch The same thing applies to creative practices. Dashboards never matched how the work actually moves. What you want is a map of decisions, references, and dead ends.
Been thinking about what this means for design practices. When making software is essentially free, the practice itself starts to look like a new kind of studio, closer to a product company than a services business.
@steipete Been thinking about this for how creative teams accumulate work. Years of proposals, components, and case studies piling up without anything reconciling them. The sweep idea applies pretty directly.
This resonates. We are still very early in how we design for working with agents.
A lot of the real innovation ahead will be in intelligent interfaces that let people see, direct, and collaborate with these systems more naturally.
a16z @speedrun request for startups: GUIs for Agents
we’re still in the MS-DOS era of agents today - CLI, terminal sessions, file directories deleted by openclaw etc. while a small slice of silicon valley are power users, we're SO early for the rest of the world
at Speedrun, we’re looking for bold founders excited to bring the power of agents to normies everywhere. there's a whole slew of products to be built here - from agent builders to marketplaces to managed infrastructure
one broad idea we’re excited about are visual abstraction layers for agents. if you don't know exactly what you want, a command line / chat interface is paralyzing - you need to see options
1 example - think of a GUI or visual command center inspired by strategy games (ex. Factorio) where agents and workflows are represented graphically. skills, tools, MCP connections, background processes, etc could all be configured and shown visually in a workspace
on UX, strategy games have long perfected agent management. zoom to get a birds-eye view of your agents, batch and queue orders via shortcuts, assign agents in multiplayer etc. a well-designed agent command center would make multi-agent orchestration for normies feel easy & intuitive
most folks today still haven't moved beyond ChatGPT. the potential is enormous - just as Windows unlocked mass-market use of personal computers, the right visual abstraction layer could unlock agentic work for everyone - from individuals to enterprise teams
if you share our vision, we'd love to chat!
Design as rendered care really resonates. What interests me about AI is whether it can help carry judgment, context, and intent all the way through the system. https://t.co/l47OgpqhgC
What feels important here is that agents need something they can actually reason over. Code carries structure, behavior, and intent more directly, which makes it a better substrate for the next generation of tools.
@sahand_io@garrytan For me the hardest part was getting the conversation loop to feel natural and dealing with service interruptions. I end up needing to reconnect Twilio constantly. When it works it's great, but getting there is fiddly. Happy to share more if you DM me.
@rvolkmn@paper@felixleezd Agree with this. The key is that it uses HTML/CSS under the hood. Claude to Figma isn’t as effective as Claude to @paper for that reason.
@andrewchen This is already happening in design. The gap between "I can describe what I want" and "I have a working prototype" is collapsing. The new skill is taste and judgment, not production.
I have run a design firm for 14 years and the hardest thing to scale has always been tribal knowledge. How to think about design as a system of objects, not a collection of screens. When to push through ambiguity versus when to ask for help. How to read a client. These things take years to develop and they live entirely in people's heads. But they don't have to. Today, that knowledge can be captured, structured, and built on.
This is really impressive. The shift from "LLMs generate UI" to "LLMs generate apps" is the right framing. Deterministic aggregations on the runtime instead of the model is the kind of decision that makes this actually usable, not just a demo. The inline mode mixing text and interactive UI is where this gets interesting for data storytelling.
1/ Got a flood of 'how to build that?' DMs on the dashboard demo
Short answer: OpenUI Lang v0.5.
It's the layer that lets the agent go beyond rendering UI and actually wire up a working app.
State, Data & Actions - the three building blocks for real Generative UI
@soleio@bhorowitz A good strategy gives people a shared frame for making local decisions. If they understand the why, they don’t need to wait for every instruction.